Selecting Items In A List With Filters
/* Create an list of items denoting the number of soldiers in each regiment, view the list */
regimentSize = (5345, 6436, 3453, 2352, 5212, 6232, 2124, 3425, 1200, 1000, 1211); regimentSize(5345, 6436, 3453, 2352, 5212, 6232, 2124, 3425, 1200, 1000, 1211)
One-line Method
This line of code does the same thing as the multiline method below, it is just more compact (but also more complicated to understand.
/* Create a list called smallRegiments that filters regimentSize to
find all items that fulfill the lambda function (which looks for all items under 2500). */
smallRegiments = list(filter((lambda x: x < 2500), regimentSize)); smallRegiments[2352, 2124, 1200, 1000, 1211]
Multi-line Method
The ease with interpreting what is happening, I’ve broken down the one-line filter method into multiple steps, one per line of code. This appears below.
/* Create a lambda function that looks for things under 2500 */
lessThan2500Filter = lambda x: x < 2500/* Filter regimentSize by the lambda function filter */
filteredRegiments = filter(lessThan2500Filter, regimentSize)/* Convert the filter results into a list */
smallRegiments = list(filteredRegiments)[2352, 2124, 1200, 1000, 1211]
For Loop Equivalent
This for loop does the same as both methods above, except it uses a for loop.
Create a for loop that go through each item of a list and finds items under 2500
/* Create a variable for the results of the loop to be placed */
smallRegiments_2 = []
/* for each item in regimentSize, */
for x in regimentSize:
/* look if the item's value is less than 2500 */
if x < 2500:
/* if true, add that item to smallRegiments_2 */
smallRegiments_2.append(x)
/* View the smallRegiment_2 variable */
smallRegiments_2[2352, 2124, 1200, 1000, 1211]
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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